1 Introduction

Here, we will apply a k-nearest neighbor (KNN) algorithm to classify the scATAC cells to a given cell type category with the help of our training set, the Multiome experiment. Remember, that KNN works on a basic assumption that data points of similar categories are closer to each other.

2 Pre-processing

2.1 Load packages

library(Seurat)
library(Signac)
library(flexclust)
library(tidyverse)
library(plyr)
library(harmony)
library(class)
library(ggplot2)
library(reshape2)

set.seed(1234)

2.2 Parameters

cell_type = "GCBC"

# Paths
path_to_obj <- str_c(
  here::here("scATAC-seq/results/R_objects/level_4/"),
  cell_type,
  "/",
  cell_type,
  "_integrated_level_4.rds",
  sep = ""
)

path_to_obj_RNA <- str_c(
  here::here("scRNA-seq/3-clustering/4-level_4/"),
  cell_type,
    "/",
  cell_type,
  "_clustered_clean_level_4_with_final_clusters.rds",
  sep = ""
)

path_to_level_4 <- here::here("scATAC-seq/results/R_objects/level_4/GCBC/")
path_to_save <- str_c(path_to_level_4, "GCBC_annotated_level_4.rds")

2.3 Variables

reduction <- "harmony"
dims <- 1:40
color_palette <-  c("#1CFFCE", "#90AD1C", "#C075A6", "#85660D", 
                    "#5A5156", "#AA0DFE", "#F8A19F", "#F7E1A0", 
                    "#1C8356", "#FEAF16", "#822E1C", "#C4451C", 
                    "#1CBE4F", "#325A9B", "#F6222E", "#FE00FA", 
                    "#FBE426", "#16FF32", "black", "#3283FE",
                    "#B00068", "#DEA0FD", "#B10DA1", "#E4E1E3",
                    "#90AD1C", "#FE00FA", "#85660D", "#3B00FB",
                    "#822E1C", "coral2", "#1CFFCE", "#1CBE4F",
                    "#3283FE", "#FBE426", "#F7E1A0", "#325A9B",   
                    "#2ED9FF", "#B5EFB5", "#5A5156", "#DEA0FD",
                    "#FEAF16", "#683B79", "#B10DA1", "#1C7F93", 
                    "#F8A19F", "dark orange", "#FEAF16", "#FBE426",  
                    "Brown")

2.4 Load data

seurat <- readRDS(path_to_obj_RNA)

seurat$level_5 <- revalue(seurat$cluster8_subcluster,
                  c("0"= "DZ/LZ",
                    "1_0"="DZ_Sphase_HistoneHigh",
                    "1_1"="DZ_G2M",
                    "2_0"="DZ-nonproliferative",
                    "2_1_0"="DZ-nonproliferative",
                    "2_1_1"="DZ-nonproliferative",
                    "3_0"="LZ",
                    "3_1"="LZ-DZ-re-entry",
                    "3_2"="LZ",
                    "4"="DZ_Sphase",
                    "5_0"="LZ-proliferative_BCL2A1neg",
                    "5_1"="LZ-proliferative_BCL2A1pos",
                    "6"="DZ_G2M",
                    "7_0"="MBC-like",
                    "7_1"="MBC-like",
                    "8_0"="MBC-like",
                    "8_1"="MBC-like",
                    "8_2"="MBC-like",
                    "9"="PC-precursors"))
tonsil_RNA_annotation <- seurat@meta.data %>%
  rownames_to_column(var = "cell_barcode") %>%
  dplyr::filter(assay == "multiome") %>%
  dplyr::select("cell_barcode", "level_5")
head(tonsil_RNA_annotation)
##                           cell_barcode             level_5
## 1 co7dzuup_xuczw9vc_AAACAGCCAAAGGTAC-1                  LZ
## 2 co7dzuup_xuczw9vc_AAACAGCCAATAGCAA-1              DZ_G2M
## 3 co7dzuup_xuczw9vc_AAACAGCCATTAAGTC-1 DZ-nonproliferative
## 4 co7dzuup_xuczw9vc_AAACCGAAGCTTACTT-1              DZ_G2M
## 5 co7dzuup_xuczw9vc_AAACCGCGTATTCGCT-1              DZ_G2M
## 6 co7dzuup_xuczw9vc_AAACCGCGTTGTAAAC-1           DZ_Sphase
DimPlot(seurat,
  group.by = "level_5",
  cols = color_palette,
  pt.size = 0.1)

seurat_ATAC <- readRDS(path_to_obj)
seurat_ATAC
## An object of class Seurat 
## 270784 features across 21447 samples within 1 assay 
## Active assay: peaks_macs (270784 features, 260336 variable features)
##  3 dimensional reductions calculated: umap, lsi, harmony
p1 <- DimPlot(seurat_ATAC,
  pt.size = 0.1)
p1

Annotation level 1 for scATAC will be defined “a priori” as unannotated and the scRNA annotation will be transfered to the scATAC-multiome cells based on the same cell barcode.

tonsil_scATAC_df <- data.frame(cell_barcode = colnames(seurat_ATAC)[seurat_ATAC$assay == "scATAC"])
tonsil_scATAC_df$level_5 <- "unannotated"

df_all <- rbind(tonsil_RNA_annotation,tonsil_scATAC_df)
rownames(df_all) <- df_all$cell_barcode
df_all <- df_all[colnames(seurat_ATAC), ]

seurat_ATAC$level_5 <- df_all$level_5
seurat_ATAC@meta.data$annotation_prob  <- 1
melt(table(seurat_ATAC$level_5))
##                          Var1 value
## 1                      DZ_G2M  1416
## 2                   DZ_Sphase   847
## 3       DZ_Sphase_HistoneHigh  1297
## 4         DZ-nonproliferative  1375
## 5                       DZ/LZ  1537
## 6                          LZ   654
## 7              LZ-DZ-re-entry   303
## 8  LZ-proliferative_BCL2A1neg   383
## 9  LZ-proliferative_BCL2A1pos   442
## 10                   MBC-like   131
## 11              PC-precursors   179
## 12                unannotated 12883
table(is.na(seurat_ATAC$level_5))
## 
## FALSE 
## 21447

2.5 General low-dimensionality representation of the assays

DimPlot(seurat_ATAC,
  group.by = "level_5",
  split.by = "assay",
  cols = color_palette,
  pt.size = 0.5)

3 KNN Algorithm

3.1 Data Splicing

Data splicing basically involves splitting the data set into training and testing data set.

reference_cells <- colnames(seurat_ATAC)[seurat_ATAC$assay == "multiome"]
query_cells <- colnames(seurat_ATAC)[seurat_ATAC$assay == "scATAC"]

reduction_ref <- seurat_ATAC@reductions[[reduction]]@cell.embeddings[reference_cells, dims]
reduction_query <- seurat_ATAC@reductions[[reduction]]@cell.embeddings[query_cells, dims]

3.2 Cross-validation of the K parameter.

We’re going to calculate the number of observations in the training dataset that correspond to the Multiome data. The reason we’re doing this is that we want to initialize the value of ‘K’ in the KNN model. To do that, we split our training data in two part: a train.loan, that correspond to the random selection of the 70% of the training data and the test.loan, that is the remaining 30% of the data set. The first one is used to traint the system while the second is uses to evaluate the learned system.

dat.d <- sample(1:nrow(reduction_ref),
               size=nrow(reduction_ref)*0.7,replace = FALSE) 

train.loan  <- reduction_ref[dat.d,] # 70% training data
test.loan <- reduction_ref[-dat.d,] # remaining 30% test data

train.loan_labels <- seurat_ATAC@meta.data[row.names(train.loan),]$level_5
test.loan_labels <- seurat_ATAC@meta.data[row.names(test.loan),]$level_5

k.optm <- c()
k.values <- c()

for (i in c(2,4,8,10,12,14,16,32,64,128)){
 print(i)
 knn.mod <- knn(train=train.loan, test=test.loan, cl=train.loan_labels, k=i)
 k.optm <- c(k.optm, 100 * sum(test.loan_labels == knn.mod)/NROW(test.loan_labels))
 k.values <- c(k.values,i)
}
## [1] 2
## [1] 4
## [1] 8
## [1] 10
## [1] 12
## [1] 14
## [1] 16
## [1] 32
## [1] 64
## [1] 128

Now we can plot the accuracy of the model taking in account a range of different K and selec the best one.

k.optim = data.frame(k.values,k.optm)

p3 <- ggplot(data=k.optim, aes(x=k.values, y=k.optm, group=1)) +
 geom_line() +
 geom_point() + 
 geom_vline(xintercept=14, linetype="dashed", color = "red")

p3

3.3 Building a Machine Learning model with the optimal k value.

train.loan  <- reduction_ref
test.loan <- reduction_query

train.loan_labels <- seurat_ATAC@meta.data[row.names(train.loan),]$level_5
test.loan_labels <- seurat_ATAC@meta.data[row.names(test.loan),]$level_5

knn.mod <- knn(train=train.loan, test=test.loan, cl=train.loan_labels, k=14, prob=T)

annotation_data <- data.frame(query_cells, knn.mod, attr(knn.mod,"prob"))
colnames(annotation_data) <- c("query_cells",
                               "level_5",
                               "annotation_prob")

annotation_data$level_5 <- as.character(annotation_data$level_5)
seurat_ATAC@meta.data[annotation_data$query_cells,]$level_5 <- annotation_data$level_5
seurat_ATAC@meta.data[annotation_data$query_cells,]$annotation_prob <- annotation_data$annotation_prob
seurat_ATAC$level_5 <- factor(seurat_ATAC$level_5)

3.4 Low-dimensionality representation of the assays

DimPlot(
  seurat_ATAC,
  cols = color_palette,
  group.by = "level_5",
  pt.size = 0.1)

DimPlot(
  cols = color_palette,
  seurat_ATAC,
  group.by = "level_5",
  pt.size = 0.1,  split.by = "assay")

melt(table(seurat_ATAC$level_5))
##                          Var1 value
## 1                      DZ_G2M  3305
## 2                   DZ_Sphase  1987
## 3       DZ_Sphase_HistoneHigh  2904
## 4         DZ-nonproliferative  4094
## 5                       DZ/LZ  5184
## 6                          LZ  1320
## 7              LZ-DZ-re-entry   680
## 8  LZ-proliferative_BCL2A1neg   535
## 9  LZ-proliferative_BCL2A1pos   733
## 10                   MBC-like   179
## 11              PC-precursors   526
saveRDS(seurat_ATAC, path_to_save)

3.5 Low-dimensionality representation of the prediction probability

Note that the probability of the prediction was lower in the transitioning cells and in not-defined clusters.

seurat_ATAC_scATAC = subset(seurat_ATAC, assay == "scATAC")

FeaturePlot(
  seurat_ATAC_scATAC, reduction = "umap",
  features = "annotation_prob",
  pt.size = 0.1)

4 Session Information

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/pauli/opt/anaconda3/envs/Tonsil_atlas/lib/libopenblasp-r0.3.10.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    grid      stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] reshape2_1.4.4     class_7.3-18       harmony_1.0        Rcpp_1.0.6         plyr_1.8.6         forcats_0.5.0      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4        readr_1.4.0        tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5      tidyverse_1.3.0    flexclust_1.4-0    modeltools_0.2-23  lattice_0.20-41    Signac_1.2.1.9003  SeuratObject_4.0.2 Seurat_4.0.3       BiocStyle_2.16.1  
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.2.0        fastmatch_1.1-0        igraph_1.2.6           lazyeval_0.2.2         splines_4.0.3          BiocParallel_1.22.0    listenv_0.8.0          scattermore_0.7        SnowballC_0.7.0        GenomeInfoDb_1.24.0    digest_0.6.27          htmltools_0.5.1.1      fansi_0.5.0            magrittr_2.0.1         tensor_1.5             cluster_2.1.0          ROCR_1.0-11            globals_0.14.0         Biostrings_2.56.0      modelr_0.1.8           matrixStats_0.59.0     docopt_0.7.1           spatstat.sparse_2.0-0  colorspace_2.0-2       rvest_0.3.6            blob_1.2.1             ggrepel_0.9.1          haven_2.3.1            xfun_0.18              sparsesvd_0.2          crayon_1.4.1           RCurl_1.98-1.2         jsonlite_1.7.2         spatstat.data_2.1-0    survival_3.2-7         zoo_1.8-9              glue_1.4.2             polyclip_1.10-0        gtable_0.3.0           zlibbioc_1.34.0        XVector_0.28.0         leiden_0.3.8           future.apply_1.7.0     BiocGenerics_0.36.1    abind_1.4-5            scales_1.1.1           DBI_1.1.0              miniUI_0.1.1.1         viridisLite_0.4.0      xtable_1.8-4          
##  [52] reticulate_1.20        spatstat.core_2.2-0    htmlwidgets_1.5.3      httr_1.4.2             RColorBrewer_1.1-2     ellipsis_0.3.2         ica_1.0-2              pkgconfig_2.0.3        farver_2.1.0           dbplyr_1.4.4           ggseqlogo_0.1          uwot_0.1.10.9000       deldir_0.2-10          here_1.0.1             utf8_1.2.1             labeling_0.4.2         tidyselect_1.1.1       rlang_0.4.11           later_1.2.0            cellranger_1.1.0       munsell_0.5.0          tools_4.0.3            cli_3.0.0              generics_0.1.0         broom_0.7.2            ggridges_0.5.3         evaluate_0.14          fastmap_1.1.0          yaml_2.2.1             goftest_1.2-2          fs_1.5.0               knitr_1.30             fitdistrplus_1.1-5     RANN_2.6.1             pbapply_1.4-3          future_1.21.0          nlme_3.1-150           mime_0.11              slam_0.1-48            RcppRoll_0.3.0         xml2_1.3.2             rstudioapi_0.12        compiler_4.0.3         plotly_4.9.4.1         png_0.1-7              spatstat.utils_2.2-0   reprex_0.3.0           tweenr_1.0.2           stringi_1.6.2          Matrix_1.3-4           vctrs_0.3.8           
## [103] pillar_1.6.1           lifecycle_1.0.0        BiocManager_1.30.10    spatstat.geom_2.2-0    lmtest_0.9-38          RcppAnnoy_0.0.18       data.table_1.14.0      cowplot_1.1.1          bitops_1.0-7           irlba_2.3.3            httpuv_1.6.1           patchwork_1.1.1        GenomicRanges_1.40.0   R6_2.5.0               bookdown_0.21          promises_1.2.0.1       KernSmooth_2.23-17     gridExtra_2.3          lsa_0.73.2             IRanges_2.22.1         parallelly_1.26.1      codetools_0.2-17       MASS_7.3-53            assertthat_0.2.1       rprojroot_2.0.2        withr_2.4.2            qlcMatrix_0.9.7        sctransform_0.3.2      Rsamtools_2.4.0        S4Vectors_0.26.0       GenomeInfoDbData_1.2.3 hms_0.5.3              mgcv_1.8-33            parallel_4.0.3         rpart_4.1-15           rmarkdown_2.5          Rtsne_0.15             ggforce_0.3.3          lubridate_1.7.9        shiny_1.6.0